
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from wordcloud import WordCloud, STOPWORDS
import plotly.express as px


%matplotlib inline


df = pd.read_csv("CAvideos.csv")
df.head()


# high-level overview of data shape and composition
print(df.shape)
print(df.dtypes)

(40881, 16)
video_id                  object
trending_date             object
title                     object
channel_title             object
category_id                int64
publish_time              object
tags                      object
views                      int64
likes                      int64
dislikes                   int64
comment_count              int64
thumbnail_link            object
comments_disabled           bool
ratings_disabled            bool
video_error_or_removed      bool
description               object
dtype: object


df['publish_time'] = df['publish_time'].astype('datetime64')


df['category_id'] = df['category_id'].astype(str)


df['ratings_disabled'] = df['ratings_disabled'].astype(object)


df['date'] = df['publish_time'].dt.date


df.head(2)


youtube_dic = {'1' : "Film & Animation", '2' : "Autos & Vehicles" ,'10' : "Music",
               '15': "Pets & Animals", '17' : "Sports", '18' : "Short Movies", '19' : "Travel & Events" ,
               '20' : "Gaming", '21' : "Videoblogging", '22' :"People & Blogs", '23' :"Comedy" ,
               '24' : "Entertainment", '25' : "News & Politics" ,'26' : "Howto & Style", 
               '27' : "Education", '28':"Science & Technology",'29' :"Nonprofits & Activism", 
               '30':"Movies", '43' :"Shows"}


df.replace({'category_id': youtube_dic}, inplace =True)


df= df.rename(columns = {"category_id": "category_name"})


#To know the sum of the null
df.isnull().sum()

video_id                     0
trending_date                0
title                        0
channel_title                0
category_name                0
publish_time                 0
tags                         0
views                        0
likes                        0
dislikes                     0
comment_count                0
thumbnail_link               0
comments_disabled            0
ratings_disabled             0
video_error_or_removed       0
description               1296
date                         0
dtype: int64


df['date'] = pd.to_datetime(df['date'])


df=df.dropna()


#Making sure null values has been deleted. 
df.columns

Index(['video_id', 'trending_date', 'title', 'channel_title', 'category_name',
       'publish_time', 'tags', 'views', 'likes', 'dislikes', 'comment_count',
       'thumbnail_link', 'comments_disabled', 'ratings_disabled',
       'video_error_or_removed', 'description', 'date'],
      dtype='object')


df.shape

(39585, 17)


df.describe()


df_youtube=df.copy()


sns.set_style("darkgrid")
sns.set_palette("deep")
bins = np.linspace(start=0, stop=40000, num=100)
plt.figure(figsize=[10, 5])
plt.hist(data=df_youtube, x='views', bins=bins)
plt.title('Number of Views on Youtube Videos', fontsize=16)
plt.xlabel('Number of Views')
plt.ylabel('Frequency')
plt.show()


# Compute the monthly average of views
monthly_avg = df_youtube.views.groupby([df_youtube["date"].dt.year.rename('year'), df_youtube["date"].dt.month.rename('month')]).mean()

# Plot the monthly average as a line plot
plt.figure(figsize=[10, 5])
monthly_avg.plot(kind='line')
plt.xlabel('Year-Month')
plt.ylabel('Average Views')
plt.title('Monthly Average of Views on Youtube Videos', fontsize=14)
plt.show()


def create_countplot(data, x_column, color=None, order=None):
    if color is not None and order is not None:
        sns.countplot(x=x_column, data=data, color=color, order=order)
    elif color is not None:
        sns.countplot(x=x_column, data=data, color=color)
    elif order is not None:
        sns.countplot(x=x_column, data=data, order=order)
    else:
        sns.countplot(x=x_column, data=data)


create_countplot(df_youtube, 'category_name', order=df_youtube['category_name'].value_counts().index)
plt.xlabel('Category Name', fontsize = 13)
plt.ylabel('Count', fontsize = 13)
plt.title('Frequency Of Category Name', fontsize=16)
#rotates the column names
plt.xticks(rotation=90);


comment_words = ''
stopwords = set(STOPWORDS)
 
# iterate through the csv file
for val in df_youtube.channel_title:
     
    # typecaste each val to string
    val = str(val)
 
    # split the value
    tokens = val.split()
     
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
     
    comment_words += " ".join(tokens)+" "
 
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
 
# plot the WordCloud image                      
plt.figure(figsize = (4, 4), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
 
plt.show()


comment_words = ''
stopwords = set(STOPWORDS)
 
# iterate through the csv file
for val in df_youtube.title:
     
    # typecaste each val to string
    val = str(val)
 
    # split the value
    tokens = val.split()
     
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
     
    comment_words += " ".join(tokens)+" "
 
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
 
# plot the WordCloud image                      
plt.figure(figsize = (4, 4), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
 
plt.show()


comment_words = ''
stopwords = set(STOPWORDS)
 
# iterate through the csv file
for val in df_youtube.tags:
     
    # typecaste each val to string
    val = str(val)
 
    # split the value
    tokens = val.split()
     
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
     
    comment_words += " ".join(tokens)+" "
 
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
 
# plot the WordCloud image                      
plt.figure(figsize = (4, 4), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
 
plt.show()


sns.boxplot(data=df_youtube, x='likes')
plt.title("Distribution of likes")
plt.ylabel("Likes", size=14)
plt.xlabel("count", size=14)
plt.figure(figsize=(5,10))

<Figure size 500x1000 with 0 Axes>

<Figure size 500x1000 with 0 Axes>


sns.barplot(x = 'category_name',y='likes',data = df_youtube,errorbar=None)
plt.title("The Relationship Between likes and Category Name")
plt.ylabel("likes", size=14)
plt.xlabel("category_name", size=14)
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16]),
 [Text(0, 0, 'Music'),
  Text(1, 0, 'Comedy'),
  Text(2, 0, 'Entertainment'),
  Text(3, 0, 'News & Politics'),
  Text(4, 0, 'People & Blogs'),
  Text(5, 0, 'Howto & Style'),
  Text(6, 0, 'Film & Animation'),
  Text(7, 0, 'Science & Technology'),
  Text(8, 0, 'Gaming'),
  Text(9, 0, 'Sports'),
  Text(10, 0, 'Nonprofits & Activism'),
  Text(11, 0, 'Pets & Animals'),
  Text(12, 0, 'Travel & Events'),
  Text(13, 0, 'Autos & Vehicles'),
  Text(14, 0, 'Education'),
  Text(15, 0, 'Shows'),
  Text(16, 0, 'Movies')])


# Group by video ID and sum the comment count for each group
comment_counts = df_youtube.groupby('channel_title').sum()['comment_count']

# Sort by comment count in descending order and take the top 10
top_comment_counts = comment_counts.sort_values(ascending=False)[:10]

# Create a bar plot
top_comment_counts.plot(kind='bar')
plt.title('Top 10 Channels by Comment Count')
plt.xlabel('Channel Title')
plt.ylabel('Comment Count')
plt.show()


# Correlation plot
sns.set_style("darkgrid")
sns.set_palette("deep")
column_name = ['views', 'dislikes', 'likes']
plt.figure(figsize = [8, 6])
sns.heatmap(df_youtube[column_name].corr(), annot = True, fmt = '.3f', center = 0,cmap='BrBG');


sns.scatterplot(x="views",y="likes",data=df_youtube)
plt.title('The relationship between views and likes')

Text(0.5, 1.0, 'The relationship between views and likes')


fig = px.histogram(df_youtube, x="category_name", y="views",histfunc='count' , text_auto=True)
fig.show()


sns.scatterplot(x="dislikes",y="likes",data=df_youtube,hue='views')
plt.title("The Relationship between likes, dislikes and views")

Text(0.5, 1.0, 'The Relationship between likes, dislikes and views')


sns.scatterplot(x="dislikes",y="likes",data=df_youtube,hue='category_name')
plt.title("The Relationship between likes, dislikes and category_name")

Text(0.5, 1.0, 'The Relationship between likes, dislikes and category_name')


sns.pairplot(data=df_youtube);

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.

<__array_function__ internals>:5: RuntimeWarning:

Converting input from bool to <class 'numpy.uint8'> for compatibility.


sns.relplot(x='views',y='comment_count',data=df_youtube,kind='scatter',hue='ratings_disabled')
plt.xlabel('views')
plt.ylabel('comment_count')

Text(40.8701624228395, 0.5, 'comment_count')

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	thumbnail_link	comments_disabled	ratings_disabled	video_error_or_removed	description
0	n1WpP7iowLc	17.14.11	Eminem - Walk On Water (Audio) ft. Beyoncé	EminemVEVO	10	2017-11-10T17:00:03.000Z	Eminem\|"Walk"\|"On"\|"Water"\|"Aftermath/Shady/In...	17158579	787425	43420	125882	https://i.ytimg.com/vi/n1WpP7iowLc/default.jpg	False	False	False	Eminem's new track Walk on Water ft. Beyoncé i...
1	0dBIkQ4Mz1M	17.14.11	PLUSH - Bad Unboxing Fan Mail	iDubbbzTV	23	2017-11-13T17:00:00.000Z	plush\|"bad unboxing"\|"unboxing"\|"fan mail"\|"id...	1014651	127794	1688	13030	https://i.ytimg.com/vi/0dBIkQ4Mz1M/default.jpg	False	False	False	STill got a lot of packages. Probably will las...
2	5qpjK5DgCt4	17.14.11	Racist Superman \| Rudy Mancuso, King Bach & Le...	Rudy Mancuso	23	2017-11-12T19:05:24.000Z	racist superman\|"rudy"\|"mancuso"\|"king"\|"bach"...	3191434	146035	5339	8181	https://i.ytimg.com/vi/5qpjK5DgCt4/default.jpg	False	False	False	WATCH MY PREVIOUS VIDEO ▶ \n\nSUBSCRIBE ► http...
3	d380meD0W0M	17.14.11	I Dare You: GOING BALD!?	nigahiga	24	2017-11-12T18:01:41.000Z	ryan\|"higa"\|"higatv"\|"nigahiga"\|"i dare you"\|"...	2095828	132239	1989	17518	https://i.ytimg.com/vi/d380meD0W0M/default.jpg	False	False	False	I know it's been a while since we did this sho...
4	2Vv-BfVoq4g	17.14.11	Ed Sheeran - Perfect (Official Music Video)	Ed Sheeran	10	2017-11-09T11:04:14.000Z	edsheeran\|"ed sheeran"\|"acoustic"\|"live"\|"cove...	33523622	1634130	21082	85067	https://i.ytimg.com/vi/2Vv-BfVoq4g/default.jpg	False	False	False	🎧: https://ad.gt/yt-perfect\n💰: https://atlant...

	video_id	trending_date	title	channel_title	category_id	publish_time	tags	views	likes	dislikes	comment_count	thumbnail_link	comments_disabled	ratings_disabled	video_error_or_removed	description	date
0	n1WpP7iowLc	17.14.11	Eminem - Walk On Water (Audio) ft. Beyoncé	EminemVEVO	10	2017-11-10 17:00:03	Eminem\|"Walk"\|"On"\|"Water"\|"Aftermath/Shady/In...	17158579	787425	43420	125882	https://i.ytimg.com/vi/n1WpP7iowLc/default.jpg	False	False	False	Eminem's new track Walk on Water ft. Beyoncé i...	2017-11-10
1	0dBIkQ4Mz1M	17.14.11	PLUSH - Bad Unboxing Fan Mail	iDubbbzTV	23	2017-11-13 17:00:00	plush\|"bad unboxing"\|"unboxing"\|"fan mail"\|"id...	1014651	127794	1688	13030	https://i.ytimg.com/vi/0dBIkQ4Mz1M/default.jpg	False	False	False	STill got a lot of packages. Probably will las...	2017-11-13

	views	likes	dislikes	comment_count
count	3.958500e+04	3.958500e+04	3.958500e+04	3.958500e+04
mean	1.169234e+06	4.059694e+04	2.058693e+03	5.159716e+03
std	3.437842e+06	1.345967e+05	1.931258e+04	2.189959e+04
min	7.330000e+02	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.497150e+05	2.395000e+03	1.040000e+02	4.420000e+02
50%	3.831200e+05	9.244000e+03	3.140000e+02	1.357000e+03
75%	9.831390e+05	2.967000e+04	9.760000e+02	3.821000e+03
max	1.378431e+08	5.053338e+06	1.602383e+06	1.114800e+06

by ( Amira Saleh )¶

Introduce the dataset¶

Preliminary Wrangling¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Questions:¶

Univariate Exploration¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Conclusions¶

Part I - ( Trending YouTube Video Statistics in CA )¶

by ( Amira Saleh )¶

Introduce the dataset¶

Preliminary Wrangling¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Questions:¶

Univariate Exploration¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Conclusions¶